Object Manufacturing Visualization

2. The method of claim 1, further comprising stacking a plurality of predicted thermal images, wherein the graphical overlay is presented with the plurality of predicted thermal images.

3. The method of claim 1, further comprising masking the predicted thermal image and the captured thermal image using a contone map to produce part maps.

6. The method of claim 5, further comprising comparing the set of anomaly scores to a set of thresholds to produce a set of grades over the captured thermal image.

7. The method of claim 6, further comprising color coding the graphical overlay based on the set of grades.

8. The method of claim 6, wherein the set of thresholds comprises multiples of a standard deviation of the machine learning model.

9. The method of claim 8, wherein an anomaly score that is more than two standard deviations below a mean of the machine learning model indicates an under-melted portion of an object.

10. The method of claim 8, wherein an anomaly score that is more than two standard deviations above a mean of the machine learning model indicates an over-melted portion of an object.

12. The 3D printing device of claim 11, wherein the plurality of contone maps are fusing contone maps.

13. The 3D printing device of claim 12, wherein the plurality of contone maps are detailing contone map.

15. The computer-readable medium of claim 14, wherein the stacked fusing contone maps, the stacked detailing contone maps, the stacked predicted thermal images, and the stacked captured thermal images exclude a non-object portion.